Geomechanics and Engineering

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Probabilistic analysis of tunnel collapse: Bayesian method for detecting change points

  • Zhou, Binghua (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Xue, Yiguo (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Li, Shucai (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Qiu, Daohong (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Tao, Yufan (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Zhang, Kai (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Zhang, Xueliang (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Xia, Teng (Geotechnical and Structural Engineering Research Center, Shandong University)
  • Received : 2019.01.06
  • Accepted : 2020.07.09
  • Published : 2020.08.25
⟨ Previous Next ⟩

Abstract

The deformation of the rock surrounding a tunnel manifests due to the stress redistribution within the surrounding rock. By observing the deformation of the surrounding rock, we can not only determine the stability of the surrounding rock and supporting structure but also predict the future state of the surrounding rock. In this paper, we used grey system theory to analyse the factors that affect the deformation of the rock surrounding a tunnel. The results show that the 5 main influencing factors are longitudinal wave velocity, tunnel burial depth, groundwater development, surrounding rock support type and construction management level. Furthermore, we used seismic prospecting data, preliminary survey data and excavated section monitoring data to establish a neural network learning model to predict the total amount of deformation of the surrounding rock during tunnel collapse. Subsequently, the probability of a change in deformation in each predicted section was obtained by using a Bayesian method for detecting change points. Finally, through an analysis of the distribution of the change probability and a comparison with the actual situation, we deduced the survey mark at which collapse would most likely occur. Surface collapse suddenly occurred when the tunnel was excavated to this predicted distance. This work further proved that the Bayesian method can accurately detect change points for risk evaluation, enhancing the accuracy of tunnel collapse forecasting. This research provides a reference and a guide for future research on the probability analysis of tunnel collapse.

Keywords

Acknowledgement

Much of the work presented in this paper was supported by the National Natural Science Foundations of China (grant numbers 41877239, 51379112, 51422904 and 41772298), and the State Key Development Program for Basic Research of China (grant number 2013CB036002), and the Fundamental Research Funds of Shandong University (grant number 2018JC044), and Shandong Provincial Natural Science Foundation (grant number JQ201513). The authors would like to express appreciation to the reviewers for their valuable comments and suggestions that helped improve the quality of our paper.

References

  1. Alimoradi, A., Moradzadeh, A., Naderi, R., Salehi, M.Z. and Etemadi, A. (2008), "Prediction of geological hazardous zones in front of a tunnel face using TSP-203 and artificial neural networks", Tunn. Undergr. Sp. Technol., 23(6), 711-717. https://doi.org/10.1016/j.tust.2008.01.001.
  2. Barry, D. and Hartigan, J.A. (1993), "A Bayesian analysis for change point problems", J. Am. Stat. Assoc., 88(421), 309-319. https://doi.org/10.1080/01621459.1993.10594323.
  3. Cevik, A., Sezer, E.A., Cabalar, A.F. and Gokceoglu, C. (2011), "Modeling of the uniaxial compressive strength of some claybearing rocks using neural network", Appl. Soft Comput., 11(2), 2587-2594. https://doi.org/10.1016/j.asoc.2010.10.008.
  4. Duan, K., Kwok, C.Y. and Ma, X.D. (2016), "DEM simulations of sandstone under true triaxial compressive tests", Acta Geotech., 12(3), 495-510. https://doi.org/10.1007/s11440-016-0480-6.
  5. Funahashi, K. (1989), "On the approximate realization of continuous mappings by neural networks", Neural Networks, 2(3), 183-192. https://doi.org/10.1016/0893-6080(89)90003-8.
  6. Gong, F.Q., Li, X.B. and Zhang, W. (2008), "Over-excavation forecast of ground opening by using Bayes discriminant analysis method", J. Cent. South Univ. Technol., 15(4), 498-502. https://doi.org/10.1007/s11771-008-0094-8.
  7. Gong, X.B., Han, L.G., Niu, J.J., Zhang, X.P., Wang, D.L. and Du, L.Z. (2010), "Combined migration velocity model-building and its application in tunnel seismic prediction", Appl. Geophys., 7(3), 265-271. https://doi.org/10.1007/s11770-010-0251-3.
  8. Huang, F., Zhao, L.H., Ling, T.H. and Yang, X.L. (2017), "Rock mass collapse mechanism of concealed karst cave beneath deep tunnel", Int. J. Rock Mech. Min. Sci., 91, 133-138. https://doi.org/10.1016/j.ijrmms.2016.11.017.
  9. Jetschny, S., Bohlen, T. and De Nil, D. (2010), "On the propagation characteristics of tunnel surface-waves for seismic prediction", Geophys. Prospec., 58(2), 245-256. https://doi.org/10.1111/j.1365-2478.2009.00823.x.
  10. Li, P.F., Zhao, Y. and Zhou, X.J. (2016), "Displacement characteristics of high-speed railway tunnel construction in loess ground by using multi-step excavation method", Tunn. Undergr. Sp. Technol., 51, 41-55. https://doi.org/10.1016/j.tust.2015.10.009.
  11. Li, S.C., Liu, B., Xu, X.J., Nie, L.C., Liu, Z.Y., Song, J., Sun, H.F., Chen, L. and Fan, K.R. (2017), "An overview of ahead geological prospecting in tunneling", Tunn. Undergr. Sp. Technol., 63, 69-94. https://doi.org/10.1016/j.tust.2016.12.011.
  12. Li, Y.Y., Zheng, Y.R. and Kang, N. (2015), "Sensitivity analysis on influencing factors of tunnel stability", Chin. J. Undergr. Sp. Eng., 11(2), 491-498.
  13. Liu, Y.L., Huang, X.L., Duan, J. and Zhang, H.M. (2017), "The assessment of traffic accident risk based on grey relational analysis and fuzzy comprehensive evaluation method", Nat. Hazards, 88(3), 1409-1422. https://doi.org/10.1007/s11069-017-2923-2.
  14. Ma, G.W. and Fu, G.Y. (2014), "A rational and realistic rock mass modelling strategy for the stability analysis of blocky rock mass", Geomech. Geoeng., 9(2), 113-123. https://doi.org/10.1080/17486025.2013.871067.
  15. Ocak, I. and Seker, S.E. (2013), "Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes", Environ. Earth Sci., 70(3), 1263-1276. https://doi.org/10.1007/s12665-012-2214-x.
  16. Pan, Q.J. and Dias, D. (2016), "The effect of pore water pressure on tunnel face stability", Int. J. Numer. Anal. Meth. Geomech., 40(15), 2123-2136. https://doi.org/10.1002/nag.2528.
  17. Perreault, L., Bernier, J., Bobee, B. and Parent, E. (2000), "Bayesian change-point analysis in hydrometeorological time series", J. Hydrol., 235(3-4), 221-241. https://doi.org/10.1016/S0022-1694(00)00270-5.
  18. Qin, C.B. and Chian, S.C. (2017), "2D and 3D stability analysis of tunnel roof collapse in stratified rock: A kinematic approach", Int. J. Rock Mech. Min. Sci., 100, 269-277. https://doi.org/10.1016/j.ijrmms.2017.10.027.
  19. Rezaei, M., Asadizadeh, M., Majdi, A. and Hossaini, M.F. (2015), "Prediction of representative deformation modulus of longwall panel roof rock strata using Mamdani fuzzy system", Int. J. Min. Sci. Technol., 25(1), 23-30. https://doi.org/10.1016/j.ijmst.2014.11.007.
  20. Senent, S., Mollon, G. and Jimenez, R. (2013), "Tunnel face stability in heavily fractured rock masses that follow the Hoek-Brown failure criterion", Int. J. Rock Mech. Min. Sci., 60, 440-451. https://doi.org/10.1016/j.ijrmms.2013.01.004.
  21. Shi, S.S., Li, S.C., Li, L.P., Zhou, Z.Q. and Wang, J. (2014), "Advance optimized classification and application of surrounding rock based on fuzzy analytic hierarchy process and Tunnel Seismic Prediction", Automat. Constr., 37(1), 217-222. https://doi.org/10.1016/j.autcon.2013.08.019.
  22. Specht, D.F. (1990), "Probabilistic neural networks", Neural Networks, 3(1), 109-118. https://doi.org/10.1016/0893-6080(90)90049-Q.
  23. Su, Y.H., Li, X., Zhao, M.H. and Xie, Z.Y. (2010), "Failure probability calculation for surrounding rock stability of tunnel considering random parameter distribution characteristics", Chin. J. Comput. Mech., 27(1), 120-126.
  24. Tian, M., Li, D.Q., Cao, Z.J., Phoon, K.K. and Wang, Y. (2016), "Bayesian identification of random field model using indirect test data", Eng. Geol., 210, 197-211. https://doi.org/10.1016/j.enggeo.2016.05.013.
  25. Wang, J., Li, S.C., Li, L.P., Shi, S.S., Xu, Z.H. and Lin, P. (2017a), "Collapse risk evaluation method on Bayesian network prediction model and engineering application", Adv. Comput. Des., 2(2), 121-131. https://doi.org/10.12989/acd.2017.2.2.121.
  26. Wang, X.T., Li, S.C., Ma, X.Y., Xue, Y.G., Hu, J. and Li, Z.Q. (2017b), "Risk assessment of rockfall hazards in a tunnel portal section based on normal cloud model", Pol. J. Environ. Stud., 26(5), 2295-2306. https://doi.org/10.15244/pjoes/68427.
  27. Xue, X.H. and Xiao, M. (2017), "Deformation evaluation on surrounding rocks of ground caverns based on PSO-LSSVM", Tunn. Undergr. Sp. Technol., 69, 171-181. https://doi.org/10.1016/j.tust.2017.06.019.
  28. Xue, Y.G., Li, S.C., Zhang, Q.S., Li., S.C., Su, M.X. and Liu, Q (2008), "Prediction and early-warning technology of geological hazards in tunnel informational construction", J. Shandong Univ. Eng. Sci., 38(5), 25-30.
  29. Xue, Y.G., Zhang, X.L., Li, S.C., Qiu, D.H., Su, M.X., Li, L.P., Li, Z.Q. and Tao, Y.F. (2018), "Analysis of factors influencing tunnel deformation in loess deposits by data mining: A deformation prediction model", Eng. Geol., 232, 94-103. https://doi.org/10.1016/j.enggeo.2017.11.014.
  30. Yagiz, S. (2011), "P-wave velocity test for assessment of geotechnical properties of some rock materials", B. Mater. Sci., 34(4), 947-953. https://doi.org/10.1007/s12034-011-0220-3.
  31. Yeh, Y.L. and Chen, T.C. (2004), "Application of grey correlation analysis for evaluating the artificial", Can. J. Civ. Eng., 31(1), 56-64. https://doi.org/10.1139/l03-074.
  32. Yuan, Y.C., Li, S.C., Li, L.P., Lei, T., Wang, S. and Sun, B.L. (2016), "Risk evaluation theory and method of collapse in mountain tunnel and its engineering applications", J. Central South Univ., 47(7), 2406-2414.
  33. Zhang, G.H., Jiao, Y.Y., Chen, L.B., Wang, H. and Li, S.C. (2015), "Analytical model for assessing collapse risk during mountain tunnel construction", Can. Geotech. J., 53(2), 326-342. https://doi.org/10.1139/cgj-2015-0064.
  34. Zhang, Y.Y., Jia, Y., Li, M. and Hou, L. (2018), "Spatiotemporal variations and relationship of PM and gaseous pollutants based on gray correlation analysis", J. Environ. Sci. Health Part ATox/Hazard Subst. Environ. Eng., 53(2), 139-145. https://doi.org/10.1080/10934529.2017.1383122.
  35. Zhou, Z.Q., Li, S.C., Li, L.P., Shi, S.S. and Xu, Z.H. (2015), "An optimal classification method for risk assessment of water inrush in karst tunnels based on grey system theory", Geomech. Eng., 8(5), 631-647. https://doi.org/10.12989/gae.2015.8.5.631.

Cited by

  1. Landslide risk assessment based on combination weighting-improved TOPSIS vol.769, pp.3, 2020, https://doi.org/10.1088/1755-1315/769/3/032022
  2. Risk Assessment of Debris Flow Disaster Based on Principal Component Analysis vol.781, pp.2, 2020, https://doi.org/10.1088/1755-1315/781/2/022111